Clustering analysis research based on DNA genetic algorithm

Authors:
Wenke Zang;Xiyu Liu;Yanlong Wang
Affiliations:
School of Management Science and Engineering, Shandong Normal University, China;School of Management Science and Engineering, Shandong Normal University, China;College of Information Technical Science, Nankai University, Tianjin, China
Venue:
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Year:
2012

Citing 3
Cited 0

Application of neural networks as a non-linear modelling technique in food mycology

Expert Systems with Applications: An International Journal
On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets

Information Sciences: an International Journal
Optimal design of TS fuzzy control system based on DNA-GA and its application

LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article proposes a fuzzy C-means clustering analysis method, which is based on DNA genetic algorithm. DNA encoding is used to analyze the center of the cluster and the quality of clustering is judged by eigenvectors and the sum of Euclidean distance of the corresponding cluster center. Through selection, crossover, mutation and inversion operation the encoding of cluster centers can be optimized, thus to get the best cluster center of cluster division. According to the simulation results the effect of this method is superior to the genetic algorithm of fuzzy C-means clustering analysis.